Reinforcement Learning in Blockchain-Enabled IIoT Networks: A Survey of Recent Advances and Open Challenges

Furqan Jameel, Uzair Javaid, Wali Ullah Khan, Muhammad Naveed Aman, Haris Pervaiz and Riku Jäntti
Additional contact information
Furqan Jameel: Department of Communications and Networking, Aalto University, 02150 Espoo, Finland
Uzair Javaid: Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
Wali Ullah Khan: School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Muhammad Naveed Aman: School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
Haris Pervaiz: School of Computing and Communications, Lancaster University, Lancaster LA1 4WA, UK
Riku Jäntti: Department of Communications and Networking, Aalto University, 02150 Espoo, Finland

Sustainability, 2020, vol. 12, issue 12, 1-23

Abstract: Blockchain is emerging as a promising candidate for the uberization of Internet services. It is a decentralized, secure, and auditable solution for exchanging, and authenticating information via transactions, without the need of a trusted third party. Therefore, blockchain technology has recently been integrated with industrial Internet-of-things (IIoT) networks to help realize the fourth industrial revolution, Industry 4.0. Though blockchain-enabled IIoT networks may have the potential to support the services and demands of next-generation networks, the gap analysis presented in this work highlights some of the areas that need improvement. Based on these observations, the article then promotes the utility of reinforcement learning (RL) techniques to address some of the major issues of blockchain-enabled IIoT networks such as block time minimization and transaction throughput enhancement. This is followed by a comprehensive case study where a Q-learning technique is used for minimizing the occurrence of forking events by reducing the transmission delays for a miner. Extensive simulations have been performed and the results have been obtained for the average transmission delay which relates to the forking events. The obtained results demonstrate that the Q-learning approach outperforms the greedy policy while having a reasonable level of complexity. To further develop the blockchain-enabled IIoT networks, some future research directions are also documented. While this article highlights the applications of RL techniques in blockchain-enabled IIoT networks, the provided insights and results could pave the way for rapid adoption of blockchain technology.

Keywords: blockchain; Industrial Internet-of-things (IIoT); Industry 4.0; Q-learning; reinforcement learning (RL) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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